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Deep learning, artificial intelligence leading the way to smart houses

Deep learning, artificial intelligence leading the way to smart houses

Plans for smart houses in the future are slowly becoming more and more plausible. A house that does all the manual labor for the occupants, where dinner is ready on the kitchen table and all the amenities in a house are included in these plans. Thanks to Baylor University’s School of Electrical and Computer Engineering and research, a future with smart houses is getting closer.

SwissCognitiveListed among the research opportunities in the School of Electrical and Computer Engineering is . This research is helping () to develop into what is presented in science fiction novels and television shows.

Dr. Liang Dong , an associate professor of electrical and computer engineering, is leading this research. Dong has been with for about three years, but only brought this research into Baylor two years ago. The research is being funded not only by Baylor, but by Intel and a prospective new funder, the United States’ Department of Defense (DOD).

Intel is interested in the research going on in Dong’s research, while the DOD is interested in applying in combat.

“The computer teaches itself,” Dong said. “ is more to mimic a human brain.”

Through the use of algorithms and data, computers are able to compare results against many other previous studies. So far, the project is being tailored for the specific use of analyzing medical images like from positron emission tomography (PET) scans and computed tomography (CT) scans in hospitals. This would help to more accurately catch the development of cancer and other diseases. The research — conducted at the Baylor Research and Innovation Collaborative (BRIC) — is essentially split up into two categories.

The theoretical research is composed of distributed and energy-efficient . Distributed deals with investigating how to use several local machines to compute different parts of the main neural network. It solves the problem of the large amount of time it takes to train a deep neural network in a single machine. Energy-efficient focuses on the problem of being able to provide a constant source of energy for necessary continuous projects. […]

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